Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient serial episode mining with minimal occurrences
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
String analysis technique for shopping path in a supermarket
Journal of Intelligent Information Systems
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Recently, supermarkets have been using RFID tags attached to shopping carts to track customers' in-store movements and to collect data on their paths. Path data obtained from customers' movements recorded in a spatial configuration contain valuable information for marketing. Customers' purchase behavior and their in-store movements can be analyzed not only by using path data but also by combining it with POS data. However, the volume of path data is very large, since the position of a cart is updated every second. Therefore, an efficient algorithm must be used to handle these data. In this paper, we apply LCMseq to shopping path data to extract promising sequential patterns with the purpose of comparing prime customers' in-store movements with those of general customers. LCMseq is an efficient algorithm for enumerating all frequent sequence patterns. Finally, we construct a decision tree model using the extracted patterns to determine prime customers' instore movements.